Patents by Inventor Guillame Lavaure

Guillame Lavaure has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Automatic rotating-machine fault diagnosis with confidence level indication
    Patent number: 10533920
    Abstract: Automatic fault diagnosis is performed on vibration data sensed from a machine. A set of faults to screen for is identified from the machine configuration. For each fault there are characteristic symptoms. For each characteristic symptom, there is a corresponding indication used to diagnose the symptom. The indications are based on analyses of the current vibration data. The diagnosed symptoms have weights assigned according to a Bayesian network, and are used to derive a Bayesian probability for the fault. A fault having a Bayesian probability exceeding a threshold value is identified as being present in the machine. For each fault a confidence level is derived. The confidence level for a first fault is based on a similarity between characteristic symptoms for the first fault and characteristic symptoms for each one of the other faults being screened.
    Type: Grant
    Filed: August 5, 2014
    Date of Patent: January 14, 2020
    Assignee: ACOEM France
    Inventors: Bertrand Wascat, Guillame Lavaure, Kamel Mekhnacha, Patrick Labeyrie, Thierry Mazoyer
  • Automatic Rotating-Machine Fault Diagnosis With Confidence Level Indication
    Publication number: 20160041070
    Abstract: Automatic fault diagnosis is performed on vibration data sensed from a machine. A set of faults to screen for is identified from the machine configuration. For each fault there are characteristic symptoms. For each characteristic symptom, there is a corresponding indication used to diagnose the symptom. The indications are based on analyses of the current vibration data. The diagnosed symptoms have weights assigned according to a Bayesian network, and are used to derive a Bayesian probability for the fault. A fault having a Bayesian probability exceeding a threshold value is identified as being present in the machine. For each fault a confidence level is derived. The confidence level for a first fault is based on a similarity between characteristic symptoms for the first fault and characteristic symptoms for each one of the other faults being screened.
    Type: Application
    Filed: August 5, 2014
    Publication date: February 11, 2016
    Applicant: 01dB-METRAVIB, Société par Actions Simplifiée
    Inventors: Bertrand Wascat, Guillame Lavaure, Kamel Mekhnacha, Patrick Labeyrie, Thierry Mazoyer